df[df['Customer Country'].isin(['United States', 'Puerto Rico'])] # Filter rows based on values in a list and select spesific columns df[["Customer Id", "Order Region"]][df['Order Region'].isin(['Central America', 'Caribbean'])] # Using NOT isin for filtering rows df[~df['C...
方法一:df[columns] 先看最简单的情况。输入列名,选择一列。例如: df['course2'] 输出结果为: 1 90 2 85 3 83 4 88 5 84 Name: course2, dtype: int64 df[column list]:选择列。例如: df[['course2','fruit']] 输出结果为: course2fruit 1 90 apple 2 85 banana 3 83 apple 4 88 oran...
"age"]]#通过列表选取多列#对于seriesdf["赋值"][0:10]#表示选取series的前9列#此刻需要注意的是如果名中含有空格,直接选取会报错如df['温度 ℃']df.rename(columns={'温度 ℃':'温度'}, inplace=True)#此时可对列明重新命名#或者通过get函数#参数解释:get(self, key,...
# Create a DataFrameobjectstu_df= pd.DataFrame(students, columns =['Name','Age','Section'], index=['1','2','3','4']) # Iterate over the sequence of column names #inreverse orderforcolumninreversed(stu_df.columns): # Select column contents by column # nameusing[]operatorcolumnSeries...
In [51]: df1 = pd.DataFrame(np.random.randn(6, 4), ...: index=list('abcdef'), ...: columns=list('ABCD')) ...: In [52]: df1 Out[52]: A B C D a 0.132003 -0.827317 -0.076467 -1.187678 b 1.130127 -1.436737 -1.413681 1.607920 c 1.024180 0.569605 0.875906 -2.211372 d 0.974466...
df[df['bar'] == 'A'] # select out everything for variable A df.pivot(index='foo', columns='bar', values='baz') # 分别指定行索引、列属性还有value值 官网demo 代码语言:javascript 代码运行次数:0 运行 AI代码解释 In [1]: df Out[1]: date variable value 0 2000-01-03 A 0.469112 1...
df.groupby(['v_id']).agg({'pred_class': [', '.join],'pred': lambda x: list(x), 'id_part': 'first'}).reset_index() 4.删除包含特定字符串所在的行 df = pd.DataFrame({'a':[1,2,3,4], 'b':['s1', 'exp_s2', 's3','exps4'], 'c':[5,6,7,8], 'd':[3,2,5...
columns : list, default: None List of column names to select from SQL table (only used when reading a table). chunksize : int, default None If specified, return an iterator where chunksize is the number of rows to include in each chunk. 上述为官网文档参数说明:Pandas.read_sql() 首先我们...
# create a dataframedframe = pd.DataFrame(np.random.randn(4, 3), columns=list('bde'), index=['India', 'USA', 'China', 'Russia'])#compute a formatted string from each floating point value in framechangefn = lambda x: '%.2f' % x# Make...
['total'] =df.select_dtypes(include=['int']).sum(1)df['total'] =df.loc[:,'Q1':'Q4'].apply(lambda x: sum(x), axis='columns')df.loc[:, 'Q10'] = '我是新来的' # 也可以# 增加一列并赋值,不满足条件的为NaNdf.loc[df.num >= 60, '成绩...